Summary  
This chapter covers Python’s string slicing syntax, showing how to extract substrings by specifying start, end, and step indices and how to use open-ended slicing patterns. It explains both fixed-range slicing and omitting components for flexible extraction.

General domain of usage  
Text processing

import unittest
import importlib
import re

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)

class TestUserCode(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.m = importlib.import_module("user_code")
        with open("user_code.py", "r", encoding="utf-8") as f:
            cls.src = f.read()

        cls.exp_name_clean = "Ada Lovelace"
        cls.exp_has_quick = True
        cls.exp_inside_parens = "quick"
        cls.exp_o_count = 4
        cls.exp_domain = "example.com"
        cls.exp_report = f"{cls.exp_name_clean} | {cls.exp_domain} | {cls.exp_o_count}"

    def test_name_clean(self):
        ok = hasattr(self.m, "name_clean") and self.m.name_clean == self.exp_name_clean
        used_strip = re.search(r"full_name\s*\.\s*strip\s*\(\s*\)", self.src)
        _dynamic_test(self, ok and used_strip,
                      "name_clean computed correctly",
                      "name_clean computed incorrectly")

    def test_has_quick_and_o_count(self):
        ok = hasattr(self.m, "has_quick") and hasattr(self.m, "o_count")
        vals = ok and self.m.has_quick == self.exp_has_quick and self.m.o_count == self.exp_o_count
        pat_quick = re.search(r'''["']quick["']\s+in\s+sentence\s*\.\s*lower''', self.src)
        pat_count = re.search(r'''sentence\s*\.\s*lower\s*\(\s*\)\s*\.\s*count\s*\(\s*["']o["']\s*\)''', self.src)
        _dynamic_test(self, vals and pat_quick and pat_count,
                      "has_quick and o_count computed correctly",
                      "has_quick or o_count computed incorrectly")

    def test_inside_parens(self):
        ok = hasattr(self.m, "inside_parens") and self.m.inside_parens == self.exp_inside_parens
        slice_pat = re.search(r"sentence\s*\[\s*start\s*\+\s*1\s*:\s*end\s*\]", self.src)
        _dynamic_test(self, ok and slice_pat,
                      "inside_parens computed correctly",
                      "inside_parens computed incorrectly")

    def test_domain(self):
        ok = hasattr(self.m, "domain") and self.m.domain == self.exp_domain
        pat_strip = re.search(r"email\s*\.\s*strip\s*\(\s*\)\s*\.\s*lower\s*\(\s*\)", self.src)
        pat_find = re.search(r'''find\s*\(\s*["']@["']\s*\)''', self.src)
        _dynamic_test(self, ok and pat_strip and pat_find,
                      "domain computed correctly",
                      "domain computed incorrectly")

    def test_report(self):
        ok = hasattr(self.m, "report") and self.m.report == self.exp_report
        pat_f = re.search(r'''f["']\{name_clean\}.*\{SEP\}.*\{domain\}.*\{SEP\}.*\{o_count\}["']''', self.src)
        _dynamic_test(self, ok and pat_f,
                      "report computed correctly",
                      "report computed incorrectly")

if __name__ == "__main__":
    unittest.main()

test_main.py

Gain a solid understanding of Python's core data types and learn how to work with them effectively. Explore numbers, booleans, and strings through hands-on exercises and practical examples. Build confidence in performing basic operations and develop the foundational skills needed to write clean and efficient Python code.

Discover the essentials of numerical data types in Python and learn how to work with integers and floating-point numbers. Master basic arithmetic operations, explore the concept of immutability, and get hands-on experience using Python's built-in functions and tools to handle numbers efficiently in real-world scenarios.

Gain a clear understanding of the Boolean data type, a specialized subtype of numeric data that represents truth values. Although it may seem challenging at first, mastering Booleans is essential for writing logical and efficient programs. This section will help you confidently apply Boolean logic to real-world programming scenarios.

Strings allow you to work with text in Python, from displaying messages to processing user input. You'll explore how to create, modify, and manipulate strings effectively. Understanding strings is essential for writing interactive programs and communicating information clearly through your code.

Learn how to handle data types effectively to ensure accuracy and consistency in your programs. Convert values to the appropriate type, clean text before comparison, and separate numeric operations from text formatting. You'll also discover how to manage missing data deliberately and handle human-readable text differently from raw input, achieving reliable, consistent, and easy-to-read results.

Challenge: Slicing and Search Drills

Solution

Awesome!

Challenge: Slicing and Search Drills

Solution